Cardiac allograft failure as a consequence of acute rejection is one of the major factors limiting survival during the initial year after heart transplantation. Delayed diagnosis may occur as a consequence of arbitrary biopsy intervals, or as a result of sampling error. Furthermore, microvascular injury, an early sequelum of solid organ rejection, may go unrecognized via conventional histologic examination of biopsy specimens. Whereas microvascular injury has been shown to be an early sequelum of renal and skin graft rejection, the pathogenesis of cardiac allograft failure is poorly defined. The purpose of the study is to examine the hypothesis that cytokine-mediated microvascular injury occurs early in the course of cardiac rejection, and is reflected in a decline in left ventricular diastolic function. The hypothesis will be examined in human heart transplant patients and in a rat model. The initial experiments will define the strengths of association between Doppler echo indices of cardiac dysfunction and a) microvascular injury, b) immune cell phenotypes, c) cytokine expression, and d) histologic features of rejection. The strengths of these associations will then be examined in a rat model of heart transplantation. In the final set of experiments, the effects of infusion of cytokine on graft function and microvascular integrity will be studied in the rat native and transplanted heart. Further confirmation of a cause-effect relationship between cytokines and graft dysfunction will be sought by examining the effect of anti-rat cytokine monoclonal antibody in blocking the effects of cytokine. Graft function will be assessed by Doppler, 2D and M-mode echocardiography. Expression of endothelial specific antigen E1.5 will be used as a marker of microvascular integrity. The level of vascular MHC expression (HLA ABC, DR, DP, and DQ) and inflammatory cell phenotype (CD4, CD8, macrophage monocytes) will be used as markers of immune activation. Cytokine expression will be determined by in situ hybridization. Regression analysis will be used to define relationships between outcome measures and outcome variables.